Artificial intelligence (AI)-based control of imaging parameters of image-capture apparatus

ABSTRACT

An image-capture apparatus and method for an artificial intelligence (AI) based control of imaging parameters of the image-capture apparatus is provided. The image-capture apparatus controls the imaging sensor based on a set of imaging parameters associated with the imaging sensor, to acquire imaging information. The acquired imaging information includes a first object of a plurality of objects. The image-capture apparatus generates by, a neural network model, a first classification result based on the acquired imaging information and modifies one or more first imaging parameters of the set of imaging parameters based on the generated first classification result for the first object. The image-capture apparatus further controls the imaging sensor based on the modified set of imaging parameters, to reacquire the imaging information to maximize a confidence of the neural network model for the detection of the first object in the reacquired imaging information.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

None.

FIELD

Various embodiments of the disclosure relate to learning-based imageprocessing, computer vision, and camera technologies. More specifically,various embodiments of the disclosure relate to an image-captureapparatus and method for artificial intelligence (AI) based control ofimaging parameters of an image-capture apparatus.

BACKGROUND

Advancements in object detection technology have led to development ofvarious imaging devices that detect objects in image frames. An imagingdevice can typically have many configurable options, some or all ofwhich may be user configurable. These configurable options may have tobe adjusted to set values of various imaging parameters which typicallycontrol accuracy of the object detection under different imagingconditions. Some of the imaging conditions may be, for example, aposition of imaging device, distance between objects and the imagingdevice, typical speed of the objects to be detected, a weather-basedlighting condition, and the like. Typically, users have no clear idea onhow to adjust all the imaging parameters for better object detection andusually, there is a single optimal combination of imaging parametersthat results in most reliable object detection.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of described systems with some aspects of the presentdisclosure, as set forth in the remainder of the present application andwith reference to the drawings.

SUMMARY

An image-capture apparatus and method for artificial intelligence(AI)-based control of imaging parameters is provided substantially asshown in, and/or described in connection with, at least one of thefigures, as set forth more completely in the claims.

These and other features and advantages of the present disclosure may beappreciated from a review of the following detailed description of thepresent disclosure, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram that illustrates an environment for an artificialintelligence (AI)-based control of imaging parameters of animage-capture apparatus, in accordance with an embodiment of thedisclosure.

FIG. 2 is an exemplary block diagram of the image-capture apparatus ofFIG. 1, in accordance with an embodiment of the disclosure.

FIG. 3 is a diagram that illustrates an exemplary scenario for anAI-based control of imaging parameters of an image-capture apparatus, inaccordance with an embodiment of the disclosure.

FIG. 4 is a diagram that illustrates exemplary imaging informationgenerated by an imaging sensor of the image-capture apparatus of FIG. 2,in accordance with an embodiment of the disclosure.

FIG. 5 is a diagram that illustrates an exemplary scenario for licenseplate recognition (LPR) under different imaging conditions, inaccordance with an embodiment of the disclosure.

FIG. 6 is a flowchart that illustrates an exemplary method for anAI-based control of imaging parameters of an image-capture apparatus, inaccordance with an embodiment of the disclosure.

DETAILED DESCRIPTION

The following described implementations may be found in the disclosedimage-capture apparatus and method for an artificial intelligence (AI)based control of imaging parameters of the image-capture apparatus.Exemplary aspects of the disclosure may include an image-captureapparatus (for example, a video camera). The image-capture apparatusincludes an imaging sensor (for example, an active or a passive pixelsensor) and a memory which may be configured to store a neural networkmodel. The neural network model may be trained to detect object(s) (forexample a license plate, an airplane, a dog, etc.), which appear in theFoV of the imaging sensor and generate classification results for thedetected object(s). These classification results may indicate aconfidence of the neural network model for the detection of theobject(s).

Typically, the confidence of the detection/recognition of object(s) inthe acquired imaging information varies under different imagingconditions, for example, different lighting conditions. The confidenceof the detection/recognition may also depend on an object type (e.g.,license plate, aero plane, humans) and its behavior (e.g.,articulate/non-articulate motion, color, and size) in an imagingenvironment. The disclosed image-capture apparatus may modify values ofimaging parameters associated with the imaging sensor and control theimaging sensor based on modified values of imaging parameters, toacquire imaging information. Thereafter, it may be determined whetherthe confidence of the neural network model improves or degrades for thedetection of certain object(s) of interest in the acquired imaginginformation. In case the confidence of the neural network modeldegrades, the imaging parameters may be further modified to counter theeffect of the imaging condition(s) on the detection/recognition of theobject(s) and new classification results for the detection of object(s)may be generated. The above process may be repeated iteratively untilthe confidence of the neural network model may exceed a threshold value(e.g., 60%). The aforementioned process helps users to avoid hassles ofmanually configuring the imaging parameters while deploying theimage-capture apparatus to find a single optimal combination of imagingparameters that results in a most reliable object detection. This may bemore helpful for users who have no clear idea on how to adjust all theimaging parameters for better object detection.

The disclosed image-capture apparatus may provide users with options toconfigure the image-capture apparatus 102 to detect object(s) ofinterest and to select a neural network model that is trained todetect/recognize the object(s) of interest. The user may be allowed toretrieve neural network parameters of an already trained neural networkmodel from a repository of neural network models, available asdownloadable files on servers.

In comparison to traditional cameras, the image-capture apparatus 102may not use an image/video codec to encode/compress the imaginginformation acquired from the imaging sensor. Instead, the disclosedimage-capture apparatus makes use of uncompressed (or losslesscompressed) imaging information acquired directly from the imagingsensor. The uncompressed imaging information may be free from many imageartifacts, especially compression artifacts and the neural network modelmay provide better detection/recognition results (in terms of theconfidence) under different imaging conditions when the uncompressedimaging information is used to adjust the values of different imagingparameters. The use of the uncompressed imaging information may make theimage-capture apparatus as power efficient and heat reductive ascompared to traditional cameras.

FIG. 1 is a diagram that illustrates an environment for an artificialintelligence (AI)-based control of imaging parameters of animage-capture apparatus, in accordance with an embodiment of thedisclosure. With reference to FIG. 1, there is shown a diagram of anenvironment 100. The environment 100 may include an image-captureapparatus 102, a server 104, and a communication network 106 establishedbetween the image-capture apparatus 102 and the server 104. Theimage-capture apparatus 102 may include an imaging sensor 108 and aneural network model 110. The neural network model 110 may be integratedwith, for example, an image processing application on the image-captureapparatus 102. There is further shown a user 112, who may be associatedwith the image-capture apparatus 102.

The image-capture apparatus 102 may include suitable logic, circuitry,interfaces, and/or code that may be configured to control the imagingsensor 108 to acquire imaging information and generate, by the neuralnetwork model 110, classification result(s) for object(s) detected inthe acquired imaging information. The imaging sensor 108 may becontrolled based on a set of imaging parameters associated with theimaging sensor 108. The imaging information may be acquired from ascene, for example, a scene 114 that includes one of a first object 114a or a second object 114 b in a field-of-view (FoV) of the imagingsensor 108. The image-capture apparatus 102 may modify one or moreimaging parameters of the set of imaging parameters based on thegenerated classification results(s) and further control the imagingsensor 108 based on the modified set of imaging parameters, to reacquirethe imaging information. This may be performed to maximize a confidenceof the neural network model 110 for the detection of the object(s) inthe reacquired imaging information.

The functionalities of the image-capture apparatus 102 may beimplemented in portable devices, such as a high-speed computing device,or a camera, and/or non-portable devices, such as the server 104.Examples of the image-capture apparatus 102 may include, but are notlimited to, a digital camera, a digital camcorder, a camera phone, asmart phone, a mobile device, a vehicle tracker device, a surveillancecamera, a vehicle traffic monitoring device, a drone, a securitydevice/camera, a computer workstation, a mainframe computer, a handheldcomputer, or any other computing device with a capability to captureimages. In certain embodiments, the image-capture apparatus 102 may be ahandheld video cam, a traffic camera, a closed-circuit television (CCTV)camera, a body camera (e.g. a police body camera), a dash camera (e.g.,a dash camera on-board a police vehicle), or an in-vehicle camera.

The server 104 may include suitable logic, circuitry, and interfacesthat may be configured to train the neural network model 110 on trainingdatasets, which may include images and labels for desired object types.The server 104 may be configured to store the training dataset for theneural network model 110 and information related to various imagingparameters of imaging sensor 108. Examples of the server 104 mayinclude, but are not limited to a database server, a file server, a webserver, a cloud server, an application server, a mainframe server, orother types of server.

The communication network 106 may include a communication medium throughwhich the image-capture apparatus 102 and the server 104 may communicatewith each other. Examples of the communication network 106 may include,but are not limited to, the Internet, a cloud network, a WirelessFidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local AreaNetwork (LAN), or a Metropolitan Area Network (MAN). Various devices inthe environment 100 may be configured to connect to the communicationnetwork 106, in accordance with various wired and wireless communicationprotocols. Examples of such wired and wireless communication protocolsmay include, but are not limited to, at least one of a TransmissionControl Protocol and Internet Protocol (TCP/IP), User Datagram Protocol(UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP),Zig Bee, EDGE, IEEE 802.11, light fidelity (Li-Fi), 802.16, IEEE802.11s, IEEE 802.11g, multi-hop communication, wireless access point(AP), device to device communication, cellular communication protocols,and Bluetooth (BT) communication protocols.

The imaging sensor 108 may include suitable logic, circuitry,interfaces, and/or code that may be configured to acquire imaginginformation of a scene in FoV of the imaging sensor 108. The acquiredimaging information may include, for example, an uncompressed imageframe of object(s) or a lossless compressed image frame of the object(s)in the scene. The imaging information may be acquired in at least onecolor, such as a Red, Green and Blue (RGB) color, Hue, Saturation andBrightness (HSB) color, Cyan Yellow Magenta and black (CYMK) color, orLAB color (in which L stands for Luminance and A and B are chromaticcomponents). The imaging sensor 108 may have suitable opticalinstruments, such as lenses to focus on the scene and/or a particularobject-of-interest (not shown) in the scene. Examples of implementationof the imaging sensor 108 may include, but are not limited to asemiconductor charged coupled device (CCD) based imaging sensor, aComplementary metal-oxide-semiconductor (CMOS) based imaging sensor, abacklit CMOS sensor with global shutter, a silicon-on-insulator(SOI)-based single-chip imaging sensor, an N-typemetal-oxide-semiconductor based imaging sensor, a flat panel detector,or other imaging sensors.

The neural network model 110 may be referred to as a computationalnetwork or a system of artificial neurons, where each layer of theneural network model 110 may include artificial neurons as nodes.Outputs of all the nodes in the neural network model 110 may be coupledto at least one node of preceding or succeeding layer(s) of the neuralnetwork model 110. Similarly, inputs of all the nodes in the neuralnetwork model 110 may be coupled to at least one node of preceding orsucceeding layer(s) of the neural network model 110. Node(s) in a finallayer of the neural network model 110 may receive inputs from at leastone previous layer. A number of layers and a number of nodes in eachlayer may be determined from a network topology and certainhyper-parameters of the neural network model 110. Such hyper-parametersmay be set before or while training the neural network model 110 on atraining dataset of image frames.

Each node in the neural network model 110 may correspond to amathematical function with a set of parameters, tunable while the neuralnetwork model 110 is trained. These parameters may include, for example,a weight parameter, a regularization parameter, and the like. Each nodemay use the mathematical function to compute an output based on one ormore inputs from nodes in other layer(s) (e.g., previous layer(s)) ofthe neural network model 110. Examples of the neural network model 110may include, but are not limited to, a convolutional neural network(CNN), a fully convolutional neural network (FCN), a long-short termmemory (LSTM)-CNN hybrid network, an auto-encoder, a variant thereof.

The neural network model 110 may include electronic data, such as, forexample, a software program, code of the software program, libraries,applications, scripts, or other logic/instructions for execution by aprocessing device, such as the image-capture apparatus 102.Additionally, or alternatively, the neural network model 110 may beimplemented using hardware, such as a processor, a microprocessor (e.g.,to perform or control performance of one or more operations), afield-programmable gate array (FPGA), or an application-specificintegrated circuit (ASIC). In some embodiments, the neural network model110 may be implemented using a combination of both the hardware and thesoftware program.

Once trained, the neural network model 110 may be configured to bedeployed on the image-capture apparatus 102 and may be trained to detectobject(s) of specific object types, for example, the first object 114 aor the second object 114 b. The neural network model 110 may be used asan object detector and may receive imaging information as input and maygenerate a plurality of classification results for object(s) detected inthe imaging information. Each classification result may indicate aconfidence (e.g., in terms of a probability score) of the neural networkmodel 110 for a detection of an object in the imaging information.

In operation, the server 104 may include a repository of neural networkmodels, where each neural network model 110 may be trained todetect/recognize object(s) of particular object type(s). Users of theimage-capture apparatus 102 may be allowed to select an object detectiontask, for example, Automatic License-Plate Recognition (ALPR) and alsoselect the neural network model 110 for the selected object detectiontask from the repository of neural network models. For example, a userwho may want to use the image-capture apparatus 102 for the ALPR may beable to download, from the server 104, a neural network model trained onlicense plate images of vehicles. Such neural network model may bedownloaded, for example, as a file that may include a set of neuralnetwork parameters of the neural network model 110. The set of neuralnetwork parameters may include, at least one a network topology, a setof neural weights, and/or a loss function. Also, in some cases, the setof neural network parameters may include activation functions (e.g.,Rectified Linear Units) and convolution kernels to be used by the neuralnetwork model 110. Once downloaded, the user 112 may be provided with anoption on the image-capture apparatus 102 to upload the file with theset of neural network parameters.

Herein, the network topology may determine a way in which the nodes ofthe neural network model 110 are interconnected with each other to formthe neural network model 110. The network topology of the neural networkmodel 110 may specify, for example, a number of layers, a number ofneurons/nodes per layer and the interconnection structure betweenneurons in different layers of the neural network model 110. Someexamples of network topology may include, but not limited to, aninterlayer connection, an intralayer connection, a self-connection, asupra-layer connection. A neural weight may represent a strength of theconnection between two neurons. If the weight from node “1” to node “2”has a greater magnitude, it may mean that node “1” has greater influenceover node “2”. The neural weight may decide how much influence the inputto the neural network model 110 will have on the output of the neuralnetwork model 110.

The image-capture apparatus 102 may deploy the neural network model 110on the image-capture apparatus 102, for example, as part of or as anintegration for an image processing application that may be configuredfor an object detection/recognition task (e.g., ALPR). The neuralnetwork model 110 may be deployed for detection/recognition of aplurality of objects, for example, aero planes or license plate numbersof vehicles. The image-capture apparatus 102 may control the imagingsensor 108 to acquire imaging information of a scene in the FoV of theimaging sensor 108. The imaging information may be, for example,uncompressed (or raw) images or images which may be encoded with alossless or a lossy codec. In certain embodiments, a portion of theimaging information may be acquired from remote cameras (e.g., CCTVcameras) installed in different locations.

The imaging sensor 108 may be controlled based on a set of imagingparameters associated with the imaging sensor 108, in order to acquirethe imaging information. By way of example, initial values of one ormore imaging parameters of the set of imaging parameters may be set bythe image-capture apparatus 102, while values of other imagingparameters may remain same as default values. Examples of the set ofimaging parameters may include, but are not limited to, a focusparameter, an f-stop parameter, an exposure parameter, a shutter speedparameter, an aperture parameter, a gain parameter, a backlightparameter, a brightness parameter, a contrast parameter, a sharpnessparameter, a white balance parameter, a sharpness parameter, a ISOsensitivity parameter, a noise reduction parameter, a demosaicparameter, a denoise parameter, a color parameter, a high dynamic range(HDR) parameter, or a deblur parameter.

The image-capture apparatus 102 may generate, by the neural networkmodel 110, a first classification result for a first object included inthe imaging information, based on the acquired imaging information. Forexample, the acquired imaging information may be provided as an input tothe neural network model 110. Thereafter, the neural network model 110may process the acquired imaging information to detect the first objectin the acquired imaging information and generate the firstclassification result for the first object. The generated firstclassification result may indicate a confidence of the neural networkmodel 110 for the detection of the first object in the acquired imaginginformation, and more specifically. the first classification result mayinclude a probability score that may indicate the confidence of thedetection of the first object by the neural network model 110.

Typically, the confidence of detection/recognition of object(s) from theacquired imaging information varies under different imaging conditions,for example, different lighting conditions, conditions related to objectsize, object speed, distance between the object and the image-captureapparatus 102, a presence of occluding structures/objects, and the like.The confidence of the detection/recognition may also depend on an objecttype (e.g., license plate, aero plane, humans) and its behavior (e.g.,articulate/non-articulate motion, color, and size with respect to theimage-plane of the image-capture apparatus 102) in an imagingenvironment where the image-capture apparatus 102 is to be deployed. Forexample, a camera for the ALPR application may be deployed such that theFoV of the traffic camera covers a section of road with a likelihood ofacquiring license plate images of vehicles on the section of the road.For the ALPR application, the traffic camera may need to operate in bothbright lighting condition at daytime as well as low light condition atnighttime. In every condition, the accuracy or the confidence for thedetection/recognition of the object(s) in the FoV may depend on valuesof certain imaging parameters associated with the imaging sensor 108. Inorder to find values of the imaging parameters that maximize theconfidence of the neural network model 110 for the objectdetection/recognition, the image-capture apparatus 102 may executecertain operations, as described herein.

The image-capture apparatus 102 may modify one or more first imagingparameters of the set of imaging parameters based on the generated firstclassification result for the first object in the acquired imaginginformation. For example, in case of ALPR application, the shutterspeed/exposure for the imaging sensor 108 may need to be decreased atnight-time to increase exposure time of the imaging sensor 108 to morelight signals. Based on the modified set of imaging parameters, theimage-capture apparatus 102 may further control the imaging sensor 108to reacquire the imaging information to maximize the confidence of theneural network model 110 for the detection of the first object in thereacquired imaging information. For example, the image-capture apparatus102 may generate a second classification result by the application ofthe neural network model 110 on the reacquired imaging information. Theimage-capture apparatus 102 may compare the second classification resultwith the first classification result. Based on the comparison of thesecond classification result with the first classification result, itmay be determined whether the confidence of the neural network model 110for the detection/recognition of the first object in the reacquiredimaging information exceeds a threshold value.

In cases where the confidence of the neural network model 110 exceedsthe threshold value (e.g., ˜60% or ˜0.6/1), the image-capture apparatus102 may generate a first combination of values of imaging parameters forthe imaging sensor 108 based on the modified set of imaging parameters.The first combination of values of imaging parameters may be generatedfor the maximization of the confidence of the neural network model 110for the detection of the first object. The image-capture apparatus 102may store the generated first combination of values of imagingparameters for the first object in memory. Such combination of valuesmay be stored so that it may be later reused to further control theimaging sensor 108 to acquire new imaging information which is optimalfor object detection/recognition performance.

In cases where the confidence of the neural network model 110 decreasesor increases but stays below the threshold value, the image-captureapparatus 102 may repeat abovementioned operations related tomodification of imaging parameters and reacquisition of the imaginginformation based on the modified imaging parameters. These operationsmay be repeated until the confidence of the neural network model 110exceeds the threshold value.

In some embodiments, the image-capture apparatus 102 may receive a userinput for a selection of a second object, which may be associated with adifferent object type, for example. The image-capture apparatus 102 maygenerate a second combination of values of imaging parameters for theimaging sensor 108 to maximize a classification result for the secondobject in the acquired imaging information. The image-capture apparatus102 may store the generated second combination of values of imagingparameters for the second object in the memory. In this way, the user112 may be provided a functionality to modify a previous objectdetection task with a focus on object(s) of a different objecttype/specification.

FIG. 2 is an exemplary block diagram of the image-capture apparatus ofFIG. 1, in accordance with an embodiment of the disclosure. FIG. 2 isexplained in conjunction with elements from FIG. 1. With reference toFIG. 2, there is shown a block diagram 200 of the image-captureapparatus 102. The image-capture apparatus 102 may include controlcircuitry 202, a memory 204, an input/output (I/O) device 206, a networkinterface 208, and an application interface 210. The control circuitry202 may be communicatively coupled to the memory 204, the I/O device206, the network interface 208, and the application interface 210. In atleast one embodiment, the image-capture apparatus 102 may includeprovisions to capture images/videos via the imaging sensor 108 and toallow the user to view the captured images/videos and/or apply certainoperations on the captured images/videos.

The control circuitry 202 may include suitable logic, circuitry,interfaces, and/or code that may be configured to execute a set ofoperations to maximize a confidence of the neural network model 110 forthe detection of the object(s) in the imaging information acquired bythe imaging sensor 108. The control circuitry 202 may be implementedbased on a number of processor technologies known in the art. Examplesof implementations of the control circuitry 202 may be a GraphicsProcessing Unit (GPU), a Reduced Instruction Set Computing (RISC)processor, an Application-Specific Integrated Circuit (ASIC) processor,a Complex Instruction Set Computing (CISC) processor, a microcontroller,a central processing unit (CPU), and/or a combination thereof.

The memory 204 may include suitable logic, circuitry, and/or interfacesthat may be configured to store instructions executable by the controlcircuitry 202. In addition, the memory 204 may store the neural networkmodel 110 and the set of imaging parameters associated with the imagingsensor 108 for each object of the plurality of objects. Examples ofimplementation of the memory 204 may include, but are not limited to,Random Access Memory (RAM), Read Only Memory (ROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD),a Solid-State Drive (SSD), a CPU cache, and/or a Secure Digital (SD)card.

The I/O device 206 may include suitable logic, circuitry, and/orinterfaces that may be configured to act as an I/O channel/interfacebetween the user 112 and the image-capture apparatus 102. The I/O device206 may include various input and output devices, which may beconfigured to communicate with different operational components of theimage-capture apparatus 102. Examples of the I/O device 206 may include,but are not limited to, a touch screen, a keyboard, a mouse, a joystick,a microphone, and a display screen (for example, a display screen 206a).

The display screen 206 a may include suitable logic, circuitry, andinterfaces that may be configured to display the application interface210. The display screen 206 a may be a touch screen which may enable theuser 112 to provide a user input via the display screen 206 a. Thedisplay screen 206 a may be realized through several known technologiessuch as, but not limited to, at least one of a Liquid Crystal Display(LCD) display, a Light Emitting Diode (LED) display, a plasma display,or an Organic LED (OLED) display technology, or other display devices.In accordance with an embodiment, the display screen 206 a may refer toa display screen of a head mounted device (HMD), a smart-glass device, asee-through display, a projection-based display, an electro-chromicdisplay, or a transparent display.

The network interface 208 may include suitable logic, circuitry,interfaces, and/or code that may be configured to connect andcommunicate with a plurality of electronic devices, such as a computer,a smartphone, or the server 104. The network interface 208 may beconfigured to implement known technologies to support wirelesscommunication. The network interface 208 may include, but is not limitedto, an antenna, a radio frequency (RF) transceiver, one or moreamplifiers, a tuner, one or more oscillators, a digital signalprocessor, a coder-decoder (CODEC) chipset, a subscriber identity module(SIM) card, and/or a local buffer.

The network interface 208 may be configured to communicate via offlineand online wireless communication with networks, such as the Internet,an Intranet, and/or a wireless network, such as a cellular telephonenetwork, a wireless local area network (WLAN), personal area network,and/or a metropolitan area network (MAN). The wireless communication mayuse any of a plurality of communication standards, protocols andtechnologies, such as Global System for Mobile Communications (GSM),Enhanced Data GSM Environment (EDGE), wideband code division multipleaccess (W-CDMA), code division multiple access (CDMA), LTE, timedivision multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi)(such as IEEE 802.11, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, and/orany other IEEE 802.11 protocol), voice over Internet Protocol (VoIP),Wi-MAX, Internet-of-Things (IoT) technology, Machine-Type-Communication(MTC) technology, a protocol for email, instant messaging, and/or ShortMessage Service (SMS).

The application interface 210 may be configured as a medium for the user112 to interact with the image-capture apparatus 102. The applicationinterface 210 may a dynamic interface that may change according to thepreferences set by the user 112 and configuration of the image-captureapparatus 102. In some embodiments, the application interface 210 maycorrespond to a user interface of one or more applications installed onthe image-capture apparatus 102. The functions or operations executed bythe image-capture apparatus 102, as described in FIG. 1, may beperformed by the control circuitry 202. The operations of the controlcircuitry 202 are described in detail, for example, in FIGS. 3, 4, and5.

FIG. 3 is a diagram that illustrates an exemplary scenario for anAI-based control of imaging parameters of an image-capture apparatus, inaccordance with an embodiment of the disclosure. FIG. 3 is explained inconjunction with elements from FIG. 1 and FIG. 2. With reference to FIG.3, there is shown a scenario diagram 300. In the scenario diagram 300,there is shown the image-capture apparatus 102 and a scene 302 in a FoVof the image-capture apparatus 102. The image-capture apparatus 102includes the imaging sensor 108 and an Artificial Intelligence (AI)engine 304. The AI engine 304 may be a software application, forexample, an ALPR software and may be configured to include a neuralnetwork model as part of the software application.

In at least one embodiment, the image-capture apparatus 102 may need tobe configured for an object detection/recognition task and therefore,may enter into a preview mode. In the preview mode, a preview of thescene 302 may be displayed on a display screen. The display screen maybe the display screen 206 a of the image-capture apparatus 102 or anexternal display (e.g., in a traffic control room) communicativelycoupled to the image-capture apparatus 102.

The user 112 may be allowed to select an object or an object type, forexample, with a touch-input on the preview of the scene 302. Once aselection is made, the image-capture apparatus 102 may determine anobject type/object class based on the selection and set the objectdetection/recognition task based on the determined object type/objectclass. For example, if the scene 302 includes trees, roads, and vehicleswith license plates, a selection of the vehicle may result in a promptthat verifies the object type as a vehicle. Also, the user 112 mayrequest or provide further input to set ALPR of vehicles as the objectdetection/recognition task. Based on the determined object type/objectclass, the user 112 may be prompted to upload a set of neural networkparameters, as part of a neural network model. The set of neural networkparameters may include, for example, a network topology parameter, a setof neural weights, and/or a loss function. In some embodiments, the setof neural network parameters may be available as a downloadable file,which may be part of a repository of neural network models pretrained onthe server 104 for different object types/object classes. The user 112may just have to operate the image-capture apparatus 102 to browse anapplication interface that shows all the downloadable files for therepository of neural network models. The image-capture apparatus 102 mayreceive a user input for the selection of a file that includes the setof neural network parameters of a neural network model 306 that istrained on images of the determined object type/object class. Onceselected, the set of neural network parameters of the file may bedeployed as the neural network model 306 on the image-capture apparatus102.

The image-capture apparatus 102 may control the imaging sensor 108 basedon a set of imaging parameters associated with the imaging sensor 108,to acquire imaging information of the scene 302. The acquired imaginginformation may include a first object 308, which may be anobject-of-interest for an object detection/recognition task. The set ofimaging parameters may include, but are not limited to, a focusparameter, an exposure parameter, a shutter speed parameter, an apertureparameter, f-stop parameter, a gain parameter, a backlight parameter, abrightness parameter, a contrast parameter, a sharpness parameter, awhite balance parameter, a sharpness parameter, a ISO sensitivityparameter, a noise reduction parameter, a demosaic parameter, a denoiseparameter, a color parameter, a high dynamic range (HDR) parameter, or adeblur parameter. A modification of at least one imaging parameter mayresult in a change in a visual quality of the acquired imaginginformation.

The image-capture apparatus 102 may generate, by the neural networkmodel 306, a first classification result for the first object 308 basedon the acquired imaging information. For example, the neural networkmodel 306 may receive the acquired imaging information (e.g.,uncompressed image frames) as an input and may generate the firstclassification result as a soft-max classification for the first object308 based on the input. The generated first classification result mayindicate a confidence of the neural network model 306 for the detectionof the first object 308 in the acquired imaging information.Specifically, the first classification result may include a probabilityscore that indicates the confidence of the detection of the first object308 by the neural network model 306. The image-capture apparatus 102 maystore the generated first classification result in the memory 204 of theimage-capture apparatus 102.

In some embodiments, the image-capture apparatus 102 may extract aregion-of-interest 310 from the acquired imaging information. Theextracted region-of-interest 310 may include at least a portion of thefirst object 308. For example, in case of ALPR, the region-of-interest310 may include a license plate of a vehicle. In such a case, the firstclassification result for the first object 308 may be generated based onthe extracted region-of-interest 310.

Typically, the confidence of detection/recognition of object(s) from theacquired imaging information varies under different imaging conditions,for example, different lighting conditions, conditions related to objectsize, object speed, distance between the object and the image-captureapparatus 102, a presence of occluding structures/objects, and the like.The confidence of the detection/recognition may also depend on an objecttype (e.g., license plate, aero plane, humans) and its behavior (e.g.,articulate/non-articulate motion, color, and size with respect to theimage-plane of the image-capture apparatus 102) in an imagingenvironment where the image-capture apparatus 102 is deployed.

For example, a traffic camera for the ALPR application may be deployedsuch that the FoV of the traffic camera covers a section of road with alikelihood of acquiring license plate images of vehicles on the sectionof road. With such application, the traffic camera may need to operatein both bright lighting condition at daytime as well as low lightcondition at nighttime. In every condition, the accuracy or theconfidence for the detection/recognition of the object(s) in the FoV maydepend on values of certain imaging parameters associated with the imagesensor. In order to find values of the imaging parameters that maximizethe confidence of the neural network model 306 for the objectdetection/recognition, the image-capture apparatus 102 may executecertain operations, as described herein.

The image-capture apparatus 102 may modify one or more first imagingparameters of the set of imaging parameters based on the generated firstclassification result for the first object 308. The image-captureapparatus 102 may have an integrated linear and non-linear optimizer tomodify the one or more first imaging parameters of the set of imagingparameters. The one or more first imaging parameters may be modified tocounter the effect of imaging condition(s) that may lead to the firstclassification result. For example, in case of the ALPR application, thefirst classification result for a license plate (as the first object308) at day-time may indicate a high confidence (e.g., 50%) that exceedsa threshold value (e.g., 40%); however, the first classification resultfor the license plate at night-time may indicate a low confidence (e.g.,33%). The image-capture apparatus 102 may determine the low lightingcondition as a primary condition that may have led to the low confidenceand therefore, may modify the shutter speed or the exposure time of theimaging sensor 108 to improve the low confidence of thedetection/recognition.

In at least one embodiment, the image-capture apparatus 102 may comparethe generated first classification result for the first object 308 witha previous classification result generated by the neural network model306 for the first object 308. Based on the comparison, the image-captureapparatus 102 may modify one or more second imaging parameters of theset of imaging parameters. The one or more second imaging parameters maybe different from the one or more first imaging parameters.Specifically, the image-capture apparatus 102 may modify the one or moresecond imaging parameters of the set of imaging parameters based on adetermination that the confidence indicated by the generated firstclassification result is less than that by the previous classificationresult.

For example, for the first classification result, the imaginginformation may have been acquired with a focus parameter set by defaultat the center of an FoV region of the image-capture apparatus 102.However, the first object 308 may be left shifted from the center of theFoV region and therefore, may appear out-of-focus. As a result, thefirst classification result may indicate a lower confidence of theneural network model 306 in the detection/recognition of the firstobject 308, as compared to that for a previous classification result.The image-capture apparatus 102 may attempt to reacquire the imaginginformation based on a modification of the focus parameter by leftshifting a focus point of the image-capture apparatus 102 from thecenter of the FoV region.

The image-capture apparatus 102 may control the imaging sensor 108 basedon the modified one or more first imaging parameters, to reacquire theimaging information. Once the imaging information is reacquired, theimage-capture apparatus 102 may generate, by the neural network model306, a second classification result based on the reacquired imaginginformation. Thereafter, the image-capture apparatus 102 may compare thegenerated first classification result for the first object 308 with thegenerated second classification result for the first object 308. Basedon the comparison of the second classification result with the firstclassification result, it may be determined whether the confidence ofthe neural network model 306 for the detection/recognition of the firstobject 308 in the reacquired imaging information exceeds a thresholdvalue.

In cases where the confidence of the neural network model 306 exceedsthe threshold value (e.g., ˜60% or ˜0.6/1), the image-capture apparatus102 may generate a first combination of values of imaging parameters forthe imaging sensor 108 based on the modified set of imaging parameters.The first combination of values of imaging parameters may be generatedfor the maximization of the confidence of the neural network model 306for the detection of the first object 308. The image-capture apparatus102 may control the memory to store the generated first combination ofvalues of imaging parameters for the first object 308 so that the suchcombination of values may be reused later to control the imaging sensor108 to acquire imaging information for optimal objectdetection/recognition performance.

In cases where the confidence of the neural network model 306 decreasesor increases but stays below the threshold value, the image-captureapparatus 102 may repeat abovementioned operations related tomodification of imaging parameters and reacquisition of the imaginginformation based on the modified imaging parameters. These operationsmay be repeated until the confidence of the neural network model 306exceeds the threshold value.

In some embodiments, the image-capture apparatus 102 may include aplurality of imaging sensors. Each imaging sensor 108 of the pluralityof imaging sensors may be configured to detect the first object 308. Theimage-capture apparatus 102 may generate a first combination of valuesfor one imaging sensor 108 and may share the generated first combinationof values with other imaging sensors of the plurality of sensors. Otherimaging sensors may receive the generated first combination of valuesand may modify their respective imaging parameters based on the receivedfirst combination of values. In some embodiments, the server 104 maygenerate the first combination of values and further share the firstcombination of values with each imaging sensor of the plurality ofimaging sensors.

In some embodiments, the image-capture apparatus 102 may display thedetected first object 308 on a display screen. The display screen may bethe display screen 206 a of the image-capture apparatus 102 or anexternal display (e.g., in a traffic control room) communicativelycoupled to the image-capture apparatus 102. Additionally, oralternatively, the image-capture apparatus 102 may display supplementalinformation on the display screen. For example, for the ALPRapplication, the supplemental information may include an image samplebased on which the license plate may have been detected/recognized. Theimage sample may include a license plate image of the license plate andmay be overlaid with a bounding box around each individual character(e.g., L, T, C, 8, 8, 8, 8) of the license plate number on the licenseplate. Additionally, the image sample be overlaid with a confidencevalue of the detection/recognition of the license plate number anddetected characters of the license plate number.

In some embodiments, the image-capture apparatus 102 may extract aregion-of-interest 310 from the reacquired imaging information anddisplay the extracted region-of-interest 310 via the display screen 206a. In some embodiments, the extracted region-of-interest 310 may includeat least a portion of the detected first object 308 (for example, alicense plate of the car). The image-capture apparatus 102 may share theextracted region-of-interest 310 with a user device (e.g., a trafficcontrol room for ALPR application) via the communication network 106.

Although, the neural network model 306 may be already trained on theserver 104, exemplary embodiments are described herein for onlinetraining of the neural network model 306 on the image-capture apparatus102 or on the server 104. In one embodiment, the image-capture apparatus102 may receive a first user input corresponding to a selection of thefirst object 308 in the acquired imaging information displayed on adisplay screen. The display screen may either the display screen 206 aof the image-capture apparatus 102 or an external displaycommunicatively coupled to the image-capture apparatus 102.

The first user input may be received through one of an applicationinterface, gaze detection, hand gesture detection, or a touch input. Incertain cases, the user 112 may also specify, via the first user input,a type (for example, a license plate number) of the first object 308(e.g., a license plate of a vehicle) to be detected. The first userinput may be a user request to train the neural network model 306 on theimaging information acquired by the imaging sensor 108. Theimage-capture apparatus 102 may update the neural network model 306based on training of the neural network model 306 on the acquiredimaging information. In training, a set of neural weights of the neuralnetwork model 306 may be updated based on an output of the neuralnetwork model 306 for the detection of the first object 308 in theacquired imaging information.

In another embodiment, the image-capture apparatus 102 may transmit arequest to train the neural network model 306 to the server 104 based onthe received first user input. Along with the request, the image-captureapparatus 102 may also share the acquired imaging information with theserver 104. The server 104 may receive the request and the acquiredimaging information as training data. Thereafter, the server 104 maytrain the neural network model 306 on the acquired imaging informationso that the neural network model 306 may robustly detect the firstobject 308.

In another embodiment, the image-capture apparatus 102 may receive asecond user input for a selection of a second object in the acquiredimaging information. The image-capture apparatus 102 may generate asecond combination of values of imaging parameters for the imagingsensor 108 to maximize the confidence of the neural network model 306for the detection of the second object. The image-capture apparatus 102may control the memory to store the generated second combination ofvalues of imaging parameters for the second object.

FIG. 4 is a diagram that illustrates exemplary imaging informationgenerated by an imaging sensor of the image-capture apparatus of FIG. 2,in accordance with an embodiment of the disclosure. FIG. 4 is explainedin conjunction with elements from FIG. 1, FIG. 2, and FIG. 3. Withreference to FIG. 4, there is shown imaging information 400 and aplurality of aero planes (airplanes) 402 a, 402 b, and 402 c as aplurality of objects in the imaging information 400. The plurality ofaero planes 402 a, 402 b, and 402 c may include a first aero plane 402a, a second aero plane 402 b and a third aero plane 402 c. The imaginginformation 400 may be acquired by the imaging sensor 108 of theimage-capture apparatus 102, while the image-capture apparatus 102 maybe deployed in an installation environment specific for Air TrafficManagement (ATM).

The image-capture apparatus 102 may select a neural network model 404trained for aero plane detection task based on a user input. Based onthe selection of the neural network model 404, the image-captureapparatus 102 may retrieve, from the server 104, a file that include aset of neural network parameters of the neural network model 404. Onceretrieved, the image-capture apparatus 102 may store the file as theneural network model 404 in the memory 204. The neural network model 404may be trained for the aero plane detection task, i.e. to detect theplurality of aero planes 402 a, 402 b, and 402 c and generate aplurality of classification results for the detection of the pluralityof aero planes 402 a, 402 b, and 402 c. Each classification result maycorrespond to a probability score that indicates a confidence of theneural network model 404 in the detection of a respective aero plane ofthe plurality of aero plane.

The image-capture apparatus 102 may extract a first region-of-interest406 a from the acquired imaging information 400. The firstregion-of-interest 406 a may include the first aero plane 402 a. Theimage-capture apparatus 102 may generate, by the neural network model404, a first classification result based on the extracted firstregion-of-interest 406 a from the acquired imaging information. Thefirst classification result may be for the first aero plane 402 a andmay indicate a confidence of the neural network model 404 for thedetection of the first aero plane 402 a in the acquired imaginginformation.

At any time, imaging conditions in the installation environment maychange. For example, a change from sunny weather to rainy weather mayresult in a low lighting condition and therefore, the first aero plane402 a in the acquired imaging information may be underexposed to light.As a result, the first classification result for the first aero plane402 a may indicate a lower confidence for the detection of the firstaero plane 402 a as compared to that when the imaging sensor 108 isexposed under normal lighting conditions.

The image-capture apparatus 102 may modify one or more first imagingparameters of the set of imaging parameters based on the generated firstclassification result for the first aero plane 402 a. The process ofmodification of the one or more first imaging parameters is described indetail, for example, in FIG. 3. The image-capture apparatus 102 mayfurther control the imaging sensor 108 based on the modified set ofimaging parameters, to reacquire the imaging information. This may beperformed to maximize the confidence of the neural network model 404 forthe detection of the first aero plane 402 a in the reacquired imaginginformation.

FIG. 5 illustrates an exemplary scenario for license plate recognition(LPR) under different imaging conditions, in accordance with anembodiment of the disclosure. FIG. 5 is explained in conjunction withelements from FIGS. 1, 2, 3, and 4. With reference to FIG. 5, there isshown an image sample 502 a of a license plate 504 at a firsttime-instant (to) and an image sample 502 b of a license plate 506 at asecond time instant (ti). The image sample 502 a may be captured atday-time and the image sample 502 b may be captured at night-time. As aresult, the image sample 502 b may be underexposed to light, as shown inFIG. 5.

An experimental result to show a variation in a confidence of the neuralnetwork model 110 on LPR with a change in shutter speed of the imagingsensor 108, is provided in Table 1, as follows.

TABLE 1 LPR confidence vs Shutter Speed of the imaging sensor 108$\frac{1}{{Shutter}\mspace{14mu}{Speed}}$ LPR Confidence 1 0.480 100.474 25 0.468 30 0.456 50 0.465 60 0.453 100 0.484 200 0.456 480 0.348960 0.150 3000 0.000

It should be noted here that data provided in Table 1 should be merelybe taken as experimental data and should not be construed as limitingfor the present disclosure. As shown in Table 1, changes in the shutterspeed expose the imaging sensor 108 with different amounts of light. TheLPR confidence of the neural network model 110 improves as the shutterspeed decreases (or exposure time increases) at night-time to avoidacquisition of images samples that are underexposed under low lightconditions. It should be noted that the exposure time or the shutterspeed is one of the imaging parameters which may need to be modified bythe image-capture apparatus 102 to maximize a confidence in thedetection of objects, such as license plates.

In FIG. 5, there is shown a first set of bounding boxes 508 a and asecond set of bounding boxes 508 b on the image sample 502 a and theimage sample 502 b, respectively. The first set of bounding boxes 508 amay localize all the characters (6, M, B, T, 6, 1, 7) imprinted on thelicense plate 504. Similarly, the second set of bounding boxes 508 b maylocalize all the characters (L, T, C, 8, 8, 8, and 8) imprinted on thelicense plate 506.

FIG. 6 is a flowchart that illustrates an exemplary method for anAI-based control of imaging parameters of an image-capture apparatus, inaccordance with an embodiment of the disclosure. With reference to FIG.6, there is shown a flowchart 600. The operations of the exemplarymethod may be executed by any image-capture apparatus, for example, bythe image-capture apparatus 102 of FIG. 2. The operations of theflowchart 600 may start at 602 and proceed to 604.

At 604, the imaging sensor 108 may be controlled based on a set ofimaging parameters associated with the imaging sensor 108, to acquireimaging information. The acquired imaging information may include afirst object. In at least one embodiment, the control circuitry 202 maycontrol the imaging sensor 108 based on the set of imaging parameters,to acquire the imaging information.

At 606, a first classification result for the first object may begenerated by the neural network model 110 based on the acquired imaginginformation. In at least one embodiment, the control circuitry 202 maygenerate, by the neural network model 110, the first classificationresult for the first object based on the acquired imaging information.

At 608, one or more first imaging parameters of set of imagingparameters may be modified. The one or more parameters may be modifiedbased on generated first classification result for the first object inthe acquired imaging information. In at least one embodiment, thecontrol circuitry 202 may modify one or more first imaging parameters ofset of imaging parameters based on generated first classification resultfor first object.

At 610, the imaging sensor 108 may be further controlled based onmodified set of imaging parameters, to reacquire the imaginginformation. The imaging information may be reacquired to maximize theconfidence of the neural network model 110 for the detection of thefirst object in the reacquired imaging information. In at least oneembodiment, the control circuitry 202 may control the imaging sensor 108based on the modified set of imaging parameters, to reacquire theimaging information. Control may pass to end.

Various embodiments of the disclosure may provide a non-transitorycomputer readable medium and/or storage medium having stored thereon,instructions executable by a machine and/or a computer to operate animage-capture apparatus (e.g., the image-capture apparatus 102) for anartificial intelligence (AI) based control of imaging parameters of theimage-capture apparatus. The instructions may cause the machine and/orcomputer to perform operations that include controlling, based on a setof imaging parameters associated with an imaging sensor (e.g., theimaging sensor 108), the imaging sensor to acquire imaging informationcomprising a first object of the plurality of objects. The operationsfurther include generating, by the neural network model (e.g., theneural network model 110), a first classification result for the firstobject based on the acquired imaging information. The generated firstclassification result indicates a confidence of the neural network modelfor a detection of the first object in the acquired imaging information.The operations further include modifying values of one or more firstimaging parameters of the set of imaging parameters based on thegenerated first classification result and controlling, based on themodified set of imaging parameters, the imaging sensor to reacquire theimaging information to maximize the confidence of the neural networkmodel for the detection of the first object in the reacquired imaginginformation.

Certain embodiments of the disclosure may be found in an image-captureapparatus and a method for an artificial intelligence (AI) based controlof imaging parameters of an image-capture apparatus. Various embodimentsof the disclosure may provide the image-capture apparatus 102 (FIG. 1)that may include the memory 204 (FIG. 2), the imaging sensor 108, andthe control circuitry 202 (FIG. 2). The memory 204 may be configured tostore the neural network model 110 trained to detect a plurality ofobjects in a field-of-view of the imaging sensor 108. The controlcircuitry 202 may be further configured to control the imaging sensor108 to acquire the imaging information that includes a first object ofthe plurality of objects. The acquired imaging information may include,for example, an uncompressed image frame of the first object or alossless compressed image frame of the first object.

The imaging sensor 108 may be controlled based on a set of imagingparameters associated with the imaging sensor 108. The set of imagingparameters may include, for example, a focus parameter, an f-stopparameter, an exposure parameter, a shutter speed parameter, an apertureparameter, a gain parameter, a backlight parameter, a brightnessparameter, a contrast parameter, a sharpness parameter, a white balanceparameter, a sharpness parameter, a ISO sensitivity parameter, a noisereduction parameter, a demosaic parameter, a denoise parameter, a colorparameter, a high dynamic range (HDR) parameter, or a deblur parameter.

The control circuitry 202 may be further configured to generate, by theneural network model 110, a first classification result for the firstobject based on the acquired imaging information. The generated firstclassification result may indicate a confidence of the neural networkmodel 110 for a detection of the first object in the acquired imaginginformation. For example, the first classification result may include aprobability score that indicates the confidence of the detection of thefirst object by the neural network model 110.

The control circuitry 202 may be further configured to modify one ormore first imaging parameters of the set of imaging parameters based onthe generated first classification result and control, based on themodified set of imaging parameters, the imaging sensor 108 to reacquirethe imaging information. The imaging information may be reacquired tomaximize the confidence of the neural network model 110 for thedetection of the first object in the reacquired imaging information.

In at least one embodiment, the control circuitry 202 may be furtherconfigured to update the neural network model 110 based on a training ofthe neural network model 110 on the acquired imaging information. In thetraining, a set of neural weights of the neural network model 110 may beupdated based on an output of the neural network model 110 for thedetection of the first object in the acquired imaging information.

In accordance with an embodiment, the control circuitry 202 may befurther configured to receive a first user input for a selection of thefirst object. Based on the received first user input, the controlcircuitry 202 may be configured to transmit, to a server 104, a requestto train the neural network model and receive, from the server 104, thetrained neural network model based on the transmitted request.

In accordance with an embodiment, the control circuitry 202 may befurther configured to receive a user input for a selection of a filethat comprises a set of neural network parameters of the neural networkmodel 110. Based on the selection, the control circuitry 202 may deploythe set of neural network parameters as the neural network model 110 onthe image-capture apparatus 102. The set of neural network parametersmay include, but are not limited to, at least one a network topologyparameter, a set of neural weights, or a loss function.

In accordance with an embodiment, the control circuitry 202 may befurther configured to extract, by the neural network model 110, aregion-of-interest from the acquired imaging information. Theregion-of-interest may include the first object. Thereafter, the controlcircuitry 202 may be further configured to generate, by the neuralnetwork model 110, the first classification result for the first objectbased on the extracted region-of-interest.

In accordance with an embodiment, the control circuitry 202 may befurther configured to compare the generated first classification resultfor the first object with a previous classification result for the firstobject generated by the neural network model 110. Based on thecomparison, the control circuitry 202 may be further configured tomodify one or more second imaging parameters of the set of imagingparameters. The one or more second imaging parameters may be differentfrom the one or more first imaging parameters. Specifically, in somecases, the control circuitry 202 may be configured to modify the one ormore second imaging parameters based on a determination that theconfidence indicated by the generated first classification result isless than that by the previous classification result.

In accordance with an embodiment, the control circuitry 202 may befurther configured to generate a first combination of values of imagingparameters for the imaging sensor 108 based on the modified set ofimaging parameters. The first combination of values may be generated forthe maximization of the confidence of the neural network model 110 forthe detection of the first object. The memory 204 may be controlled tostore the generated first combination of values of imaging parametersfor the first object.

In accordance with an embodiment, the control circuitry 202 may befurther configured to receive a second user input for a selection of asecond object of the plurality of objects. The control circuitry 202 maybe further configured to generate a second combination of values ofimaging parameters for the imaging sensor 108 to maximize the confidenceof the neural network model 110 for the detection of the second object.The memory 204 may be controlled to store the generated secondcombination of values of imaging parameters for the second object.

The present disclosure may be realized in hardware, or a combination ofhardware and software. The present disclosure may be realized in acentralized fashion, in at least one computer system, or in adistributed fashion, where different elements may be spread acrossseveral interconnected computer systems. A computer system or otherapparatus adapted to carry out the methods described herein may besuited. A combination of hardware and software may be a general-purposecomputer system with a computer program that, when loaded and executed,may control the computer system such that it carries out the methodsdescribed herein. The present disclosure may be realized in hardwarethat includes a portion of an integrated circuit that also performsother functions.

The present disclosure may also be embedded in a computer programproduct, which includes all the features that enable the implementationof the methods described herein, and which, when loaded in a computersystem, is able to carry out these methods. Computer program, in thepresent context, means any expression, in any language, code ornotation, of a set of instructions intended to cause a system with aninformation processing capability to perform a particular functioneither directly, or after either or both of the following: a) conversionto another language, code or notation; b) reproduction in a differentmaterial form.

While the present disclosure has been described with reference tocertain embodiments, it will be understood by those skilled in the artthat various changes may be made, and equivalents may be substitutedwithout deviation from the scope of the present disclosure. In addition,many modifications may be made to adapt a particular situation ormaterial to the teachings of the present disclosure without deviationfrom its scope. Therefore, it is intended that the present disclosurenot be limited to the particular embodiment disclosed, but that thepresent disclosure will include all embodiments falling within the scopeof the appended claims.

What is claimed is:
 1. An image-capture apparatus, comprising: animaging sensor; a memory configured to store a neural network modelwhich is trained to detect a plurality of objects in a field-of-view(FOV) of the imaging sensor; and control circuitry coupled with theimaging sensor and the memory, wherein the control circuitry isconfigured to: control, based on a set of imaging parameters associatedwith the imaging sensor, the imaging sensor to acquire imaginginformation comprising a first object of the plurality of objects;generate, by the neural network model, a first classification result forthe first object based on the acquired imaging information, wherein thegenerated first classification result indicates a confidence of theneural network model for a detection of the first object in the acquiredimaging information; modify one or more first imaging parameters of theset of imaging parameters based on the generated first classificationresult; and control, based on the modified set of imaging parameters,the imaging sensor to reacquire the imaging information to maximize theconfidence of the neural network model for the detection of the firstobject in the reacquired imaging information.
 2. The image-captureapparatus according to claim 1, wherein the control circuitry is furtherconfigured to update the neural network model based on a training of theneural network model on the acquired imaging information, and in thetraining, a set of neural weights of the neural network model is updatedbased on an output of the neural network model for the detection of thefirst object in the acquired imaging information.
 3. The image-captureapparatus according to claim 1, wherein the control circuitry is furtherconfigured to: receive a user input for a selection of a file thatcomprises a set of neural network parameters of the neural networkmodel; and deploy the set of neural network parameters as the neuralnetwork model on the image-capture apparatus based on the selection,wherein the set of neural network parameters comprises at least one anetwork topology parameter, a set of neural weights, or a loss function.4. The image-capture apparatus according to claim 1, wherein the controlcircuitry is further configured to: receive a first user input for aselection of the first object; transmit, to a server, a request to trainthe neural network model based on the received first user input; andreceive, from the server, the trained neural network model based on thetransmitted request.
 5. The image-capture apparatus according to claim1, wherein the set of imaging parameters associated with the imagingsensor comprises at least one of a focus parameter, an f-stop parameter,an exposure parameter, a shutter speed parameter, an aperture parameter,a gain parameter, a backlight parameter, a brightness parameter, acontrast parameter, a sharpness parameter, a white balance parameter, asharpness parameter, a ISO sensitivity parameter, a noise reductionparameter, a demosaic parameter, a denoise parameter, a color parameter,a high dynamic range (HDR) parameter, or a deblur parameter.
 6. Theimage-capture apparatus according to claim 1, wherein the controlcircuitry is further configured to: extract, by the neural networkmodel, a region-of-interest from the acquired imaging information,wherein the region-of-interest includes the first object; and generate,by the neural network model, the first classification result for thefirst object based on the extracted region-of-interest.
 7. Theimage-capture apparatus according to claim 1, wherein the firstclassification result comprises a probability score that indicates theconfidence of the detection of the first object by the neural networkmodel.
 8. The image-capture apparatus according to claim 1, wherein thecontrol circuitry is further configured to: compare the generated firstclassification result for the first object with a previousclassification result for the first object generated by the neuralnetwork model; and modify one or more second imaging parameters of theset of imaging parameters based on the comparison, wherein the one ormore second imaging parameters are different from the one or more firstimaging parameters.
 9. The image-capture apparatus according to claim 8,wherein the control circuitry is configured to modify the one or moresecond imaging parameters of the set of imaging parameters based on adetermination that the confidence indicated by the generated firstclassification result is less than that by the previous classificationresult.
 10. The image-capture apparatus according to claim 1, whereinthe control circuitry is further configured to: generate a firstcombination of values of imaging parameters for the imaging sensor basedon the modified set of imaging parameters for the maximization of theconfidence of the neural network model for the detection of the firstobject; and control the memory to store the generated first combinationof values of imaging parameters for the first object.
 11. Theimage-capture apparatus according to claim 1, wherein the controlcircuitry is further configured to: receive a second user input for aselection of a second object of the plurality of objects; and generate asecond combination of values of imaging parameters for the imagingsensor to maximize the confidence of the neural network model for thedetection of the second object; and control the memory to store thegenerated second combination of values of imaging parameters for thesecond object.
 12. The image-capture apparatus according to claim 1,wherein the acquired imaging information comprises an uncompressed imageframe of the first object or a lossless compressed image frame of thefirst object.
 13. A method, comprising: in an image-capture apparatuswhich includes an imaging sensor and a memory: storing, by the memory, aneural network model trained to detect a plurality of objects in afield-of-view (FOV) of the imaging sensor; controlling, based on a setof imaging parameters associated with the imaging sensor, the imagingsensor to acquire imaging information comprising a first object of theplurality of objects; generating, by the neural network model, a firstclassification result for the first object based on the acquired imaginginformation, wherein the generated first classification result indicatesa confidence of the neural network model for a detection of the firstobject in the acquired imaging information; modifying one or more firstimaging parameters of the set of imaging parameters based on thegenerated first classification result; and controlling, based on themodified set of imaging parameters, the imaging sensor to reacquire theimaging information to maximize the confidence of the neural networkmodel for the detection of the first object in the reacquired imaginginformation.
 14. The method according to claim 13, further comprisingupdating the neural network model based on a training of the neuralnetwork model on the acquired imaging information, wherein in thetraining, a set of neural weights of the neural network model is updatedbased on an output of the neural network model for the detection of thefirst object in the acquired imaging information.
 15. The methodaccording to claim 13, receiving a user input for a selection of a filethat comprises a set of neural network parameters of the neural networkmodel; and deploying the set of neural network parameters as the neuralnetwork model on the image-capture apparatus based on the selection, andwherein the set of neural network parameters comprises at least one anetwork topology parameter, a set of neural weights, or a loss function.16. The method according to claim 13, further comprising: receiving afirst user input for a selection of the first object; transmitting, to aserver, a request to train the neural network model based on thereceived first user input; and receiving, from the server, the trainedneural network model based on the transmitted request.
 17. The methodaccording to claim 13, wherein the set of imaging parameters associatedwith the imaging sensor comprises at least one of a focus parameter, anexposure parameter, an f-stop parameter, a shutter speed parameter, anaperture parameter, a gain parameter, a backlight parameter, abrightness parameter, a contrast parameter, a sharpness parameter, awhite balance parameter, a sharpness parameter, a ISO sensitivityparameter, a noise reduction parameter, a demosaic parameter, a denoiseparameter, a color parameter, a high dynamic range (HDR) parameter, or adeblur parameter.
 18. The method according to claim 13, wherein thefirst classification result comprises a probability score that indicatesthe confidence of the detection of the first object by the neuralnetwork model.
 19. A non-transitory computer-readable medium havingstored thereon, computer-executable instructions that when executed byan image-capture apparatus which includes an imaging sensor and a memoryconfigured to store a neural network model, causes the image-captureapparatus to execute operations, the operations comprising, comprising:controlling, based on a set of imaging parameters associated with animaging sensor, the imaging sensor to acquire imaging informationcomprising a first object of a plurality of objects; generating, by aneural network model, a first classification result for the first objectbased on the acquired imaging information, wherein the generated firstclassification result indicates a confidence of the neural network modelfor a detection of the first object in the acquired imaging information;modifying one or more first imaging parameters of the set of imagingparameters based on the generated first classification result; andcontrolling, based on the modified set of imaging parameters, theimaging sensor to reacquire the imaging information to maximize theconfidence of the neural network model for the detection of the firstobject in the reacquired imaging information.
 20. The computer-readablemedium according to claim 19, wherein the set of imaging parametersassociated with the imaging sensor comprises at least one of a focusparameter, an f-stop parameter, an exposure parameter, a shutter speedparameter, an aperture parameter, a gain parameter, a backlightparameter, a brightness parameter, a contrast parameter, a sharpnessparameter, a white balance parameter, a sharpness parameter, a ISOsensitivity parameter, a noise reduction parameter, a demosaicparameter, a denoise parameter, a color parameter, a high dynamic range(HDR) parameter, or a deblur parameter.